Data management comprises all the disciplines related to managing data as a valuable resource.

Overview

The official definition provided by DAMA is that "Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise." This definition is fairly broad and encompasses a number of professions which may not have direct technical contact with lower-level aspects of data management, such as relational database management.

Alternatively, the definition provided in the DAMA Data Management Body of Knowledge (DAMA-DMBOK) is: "Data management is the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets."

Topics in Data Management

Topics in Data Management, grouped by the DAMA DMBOK Framework, include:

1. Data Governance
• Data asset
• Data governance
• Data steward
• Data analysis
• Data architecture
• Data modeling
• Data maintenance
• Database administration
• Database management system
• Data access
• Data privacy
• Data security
• Data cleansing
• Data integrity
• Data quality
• Data quality assurance
6. Reference and Master Data Management
• Data integration
• Master Data Management
• Reference data
• Business intelligence
• Data mart
• Data mining
• Data movement (extract, transform and load)
• Data warehousing
• Document management system
• Records management
• Meta-data management
• Metadata
• Metadata discovery
• Metadata publishing
• Metadata registry

Usage

In modern management usage, one can easily discern a trend away from the term 'data' in composite expressions to the term information or even knowledge when talking in non-technical context. Thus there exists not only data management, but also information management and knowledge management. This is a fairly detrimental tendency in that it obscures the fact that is usually always plain, traditional data that is managed or somehow processed on second looks. The extremely relevant distinction between data and derived values can be seen in the information ladder. While data can exist as such, 'information' and 'knowledge' are always in the "eye" (or rather the brain) of the beholder and can only be measured in relative units.